Gender-specific sarcopenia screening in hemodialysis: insights from lower limb strength and physiological indicators.

IF 2.2 4区 医学 Q2 UROLOGY & NEPHROLOGY
Yujie Yang, Hualong Liao, Yang Chen, Ying Qiu, Fei Yan, Ping Fu, Jirong Yue, Yu Chen, Huaihong Yuan
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引用次数: 0

Abstract

Objective: Maintenance hemodialysis (MHD) patients often suffer from sarcopenia, affecting lower limb muscle strength and increasing the risk of falls and mortality. This study aims to develop an auxiliary screening model for sarcopenia in MHD patients based on machine learning methods, utilizing lower limb muscle strength indicators, while paying attention to the gender difference and exploring its value in sarcopenia screening.

Methods: This cross-sectional study collected data from MHD patients at a hemodialysis center in China. Sarcopenia was assessed using the 2019 Asian Working Group for Sarcopenia update. A self-developed lower limb muscle strength testing device was used. Other physiological indicators, including basic information and lab findings, were collected. Participants were grouped into sarcopenia and control groups, with gender-specific binary classification models developed. Stratified shuffling and synthetic minority oversampling techniques were used to build screening classifiers.

Results: Data from 164 MHD patients were ultimately collected, including 83 males (41 with possible sarcopenia or sarcopenia) and 81 females (53 with possible sarcopenia or sarcopenia). Gender-specific binary classification models were developed using lower limb muscle strength indicators, with the male model having an AUC of 79% and the female model an AUC of 80%, respectively. Combining lower limb muscle strength with other physiological indicators improved the female model's screening capability, achieving an AUC of 90%.

Conclusion: This study demonstrates that the auxiliary screening model for sarcopenia, developed using machine learning methods, highlights the significant value of lower limb muscle strength indicators in identifying sarcopenia in MHD patients. The gender-specific screening models show good discriminatory ability across different genders, providing effective tools for the early screening and management of sarcopenia in MHD patients.

Trial registration: Chinese Clinical Trial Registry (ChiCTR2100051111), registered on 2021-09-13.

血液透析中性别特异性肌肉减少症筛查:下肢力量和生理指标的见解。
目的:维持性血液透析(MHD)患者常出现肌肉减少症,影响下肢肌肉力量,增加跌倒和死亡的风险。本研究旨在基于机器学习方法,利用下肢肌力指标,建立MHD患者肌少症的辅助筛查模型,同时关注性别差异,探索其在肌少症筛查中的价值。方法:本横断面研究收集了中国一家血液透析中心MHD患者的数据。使用2019年亚洲肌少症工作组更新对肌少症进行评估。采用自行研制的下肢肌力测试装置。收集其他生理指标,包括基本信息和实验室结果。参与者被分为肌肉减少症组和对照组,并建立了针对性别的二元分类模型。分层洗牌和合成少数过采样技术用于构建筛选分类器。结果:最终收集了164例MHD患者的数据,其中男性83例(41例可能出现肌肉减少症或肌肉减少症),女性81例(53例可能出现肌肉减少症或肌肉减少症)。采用下肢肌力指标建立性别二元分类模型,男性模型AUC为79%,女性模型AUC为80%。将下肢肌力与其他生理指标相结合,提高了女性模型的筛选能力,AUC达到90%。结论:本研究表明,利用机器学习方法建立的肌少症辅助筛查模型,突出了下肢肌力指标在MHD患者肌少症识别中的重要价值。性别特异性筛查模型在不同性别间表现出良好的区分能力,为MHD患者肌肉减少症的早期筛查和管理提供了有效的工具。试验注册:中国临床试验注册中心(ChiCTR2100051111),注册日期:20121-09-13。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Nephrology
BMC Nephrology UROLOGY & NEPHROLOGY-
CiteScore
4.30
自引率
0.00%
发文量
375
审稿时长
3-8 weeks
期刊介绍: BMC Nephrology is an open access journal publishing original peer-reviewed research articles in all aspects of the prevention, diagnosis and management of kidney and associated disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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